Prompt Chaining
Prompt Chaining is the technique of linking multiple prompts in a sequential workflow so the output of one prompt becomes the input or context for the next, enabling large language models to tackle complex, multi-step tasks. A chain might start with “Generate five marketing ideas,” feed those ideas into a second prompt that “Select the strongest idea,” and finish with a third prompt that “Write a 200-word pitch.” Chains often blend roles—planner, researcher, editor—each running with different temperature, system instructions, or retrieved documents. Frameworks such as LangChain or LangGraph orchestrate branches, retries, and guardrails (model-level function calling only supplies the structured hand-off between steps), while evaluation harnesses log token cost, latency, and success rate at every step. Benefits include modularity, improved accuracy, and easier debugging, but challenges arise with context-window limits and error propagation, mitigated by summarizing intermediate results or adding validation prompts. Prompt Chaining turns monolithic requests into structured pipelines that mirror human workflows.
Related terms
Related services: LLM development, Agentic AI consulting.
Ready to put agentic AI to work?
Book a free 45-minute consultation. We'll map one real process worth automating with production-grade AI.